Loren Data's SAM Daily™

SAMDAILY.US - ISSUE OF AUGUST 19, 2023 SAM #7935
SOURCES SOUGHT

A -- Development of Artificial Intelligence (AI) methods in chemical exposure and metabolic network modeling

Notice Date

8/17/2023 11:45:16 AM
 

Notice Type

Sources Sought
 

Contracting Office

ARMY MED RES ACQ ACTIVITY FORT DETRICK MD 21702 USA
 

ZIP Code

21702
 

Solicitation Number

PANMRA23P0000006313
 

Response Due

8/24/2023 8:00:00 AM
 

Archive Date

09/08/2023
 

Point of Contact

NATHAN ANDERSON, Phone: 3016199075
 

E-Mail Address

nathan.p.anderson19.civ@health.mil
(nathan.p.anderson19.civ@health.mil)
 

Description

The Contractor shall conduct all elements of research centered on assessment of chemical injuries, prevention thereof, and use of AI-method to predict molecular mechanisms of toxicity. The ability to predict the potential of a chemical to interact with toxicity targets is crucial for understanding the adverse liability associated with them and providing insights into the underlying mechanisms of toxicity. However, it is practically impossible to experimentally screen against hundreds of potential targets for a single chemical, and even more so for a large database of chemicals. Currently, it is not possible to experimentally screen against these new targets for every military-relevant chemical exposure The research is to develop predictive computational tools and platforms using deep neural networks to assess the mechanisms of adverse effects associated with chemical exposures, including industrial and commercial chemicals, chemical agents, toxins, and drugs. This is in response to the increasing risk of adverse health effects for military personnel who are exposed to chemicals during training exercises, overseas deployments, and national defense operations. Additionally, the potential for chemical exposures is expected to increase in the future as conflicts occur in urban, industrialized megacities. The proposed platform will utilize in vitro and in vivo experimental screening data to predict the mechanisms of adverse outcomes
 

Web Link

SAM.gov Permalink
(https://sam.gov/opp/f01c56f25099483ab1f59091c2a98398/view)
 

Place of Performance

Address: Frederick, MD 21702, USA

Zip Code: 21702

Country: USA
 

Record

SN06796136-F 20230819/230817230054 (samdaily.us)
 

Source

SAM.gov Link to This Notice
(may not be valid after Archive Date)

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